External parameters contribution in domestic load forecasting using neural network

Domestic demand prediction is very important for home energy management system and also for peak reduction in the power system network. In this work, for precise prediction of power demand, external parameters, such as temperature and solar radiation, are considered and included in the prediction model for improving prediction performance. Power prediction models for coming hours' power demand estimation are built using neural network based on hourly power consumptions data with / without ambient temperature data and global solar irradiation (GSI) data respectively. In this work, a typical Southern Norwegian household demand has been considered. As a result, both ambient temperature and GSI data are effective on improving the prediction performance on certain level, and the prediction performs better when ambient temperature and GSI are included together.